Systems that process audio signals to distinguish between harmonic sounds represented in an audio signal and noise, determine sound parameters of harmonic sounds represented in an audio signal, classify harmonic sounds represented in an audio signal by grouping harmonic sounds according to source, and/or perform other types of processing of audio are known. Such systems may be useful, for example, in detecting, recognizing, and/or classifying by speaker, human speech, which is comprised of harmonic sounds. Conventional techniques for determining sound parameters of harmonic sounds and/or classifying harmonic sounds may degrade quickly in the presence of relatively low amounts of noise (e.g., audio noise present in recorded audio signals, signal noise, and/or other noise).
Generally, conventional sound processing involves converting an audio signal from the time domain into the frequency domain for individual time windows. Various types of signal processing techniques and algorithms may then be performed on the signal in the frequency domain in an attempt to distinguish between sound and noise represented in the signal before further processing can be performed. This processed signal may then be analyzed to determine sound parameters such as pitch, envelope, and/or other sound parameters. Sounds represented in the signal may be classified.
Conventional attempts to distinguish between harmonic sound and noise (whether sonic noise represented in the signal or signal noise) may amount to attempts to “clean” the signal to distinguish between harmonic sounds and background noise. Unfortunately, often times these conventional techniques result in a loss of information about harmonic sounds represented in the signal, as well as noise. The loss of this information may impact the accuracy and/or precision of downstream processing to, for example, determine sound parameter(s) of harmonic sound, classify harmonic sounds, and/or other downstream processing.